In contrast,
methods for publication bias in meta-analysis of diagnostic test accuracy
have been understudied.
Some graphical methods have been proposed based on the
commonly used univariate measure, diagnostic odds ratio (DOR).
[1]}
conducted the simulation studies
to compare the the Begg, Egger, and Macaskill tests of funnel plot asymmetry on
the log-transformed DOR (lnDOR) for diagnostic studies.
Their results showed that those funnel plot investigations based on standard error of lnDOR
might be seriously misleading.
[2]}
compared the performance of the tests
combined with four different univariate measures of diagnostic accuracy.
Their simulation studies showed that the combination of lnDOR and trim-and-fill
was the best to detect publication bias,
but it lacked power when the number of studies in meta-analysis was small.
These graphical methods may be useful to detect publication bias,
but cannot address the relationship between TPR and FPR like SROC.
To evaluate the impact of publication bias on SROC,
[3]}
extended
the sensitivity analysis method of
[4]}, [5]}
to the meta-analysis of diagnostic test accuracy
based on the bivariate binomial model.
[6]}
proposed the conditional likelihood
to correct publication bias in sensitivity and specificity
based on the bivariate normal model.
[7]}
proposed an empirical (full) likelihood based
approach under a similar setting to
[6]}
and increased the correction accuracy.
All the methods
considered the Heckman-type selection function
on the sample size of a study.
As explained in [3]},
this Heckman-type selection function
gives a certain framework to model
selective publication mechanisms in the diagnostic studies;
a drawback is that
it is completely free from
the cutoff values.
Given different cutoff values,
the measures representing the test accuracy will also change.
If some statistical hypothesis testing was used in the study,
the statistical significance, \(p\) -value, conditional on the cutoff value
would be influential in the selective publication process;
accordingly,
selection functions allowing study-specific cutoff values
would be appealing.
